Temporal Abstraction-based Clinical Phenotyping with Eureka!

• Type of Material: Article
• Authors: Andrew Post, Emory University; Tahsin Kurc, Emory University; Richie Willard, Emory University; Himanshu Rathod, Emory University; Michel Mansour, Emory University; Akshatha Kalsanka Pai, Emory University; William M. Torian, Emory University; Sanjay Agravat, Emory University; Suzanne Sturm, Emory University; Joel Saltz, Emory University
• Language: English
• Date: 2013-11-16
• Publisher: AMIA
• Publication Version: Final Published Version
• Copyright Statement: ©2013 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
• Final Published Version (URL): https://knowledge.amia.org/
• Title of Journal or Parent Work: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
• Volume: 2013
• Issue: 2013
• Start Page: 1160
• End Page: 1169
• Grant/Funding Information: This work was supported in part by NHLBI grant R24 HL085343; PHS Grant UL1 RR025008, KL2 RR025009 and TL1 RR025010 from the CTSA program, NIH, NCRR; NIMHD Grant RC4MD005964; Emory Healthcare; and Emory Winship Cancer Institute.
• Research Categories: Health Sciences, General; Engineering, Biomedical

Abstract

Temporal abstraction, a method for specifying and detecting temporal patterns in clinical databases, is very expressive and performs well, but it is difficult for clinical investigators and data analysts to understand. Such patterns are critical in phenotyping patients using their medical records in research and quality improvement. We have previously developed the Analytic Information Warehouse (AIW), which computes such phenotypes using temporal abstraction but requires software engineers to use. We have extended the AIW’s web user interface, Eureka! Clinical Analytics, to support specifying phenotypes using an alternative model that we developed with clinical stakeholders. The software converts phenotypes from this model to that of temporal abstraction prior to data processing. The model can represent all phenotypes in a quality improvement project and a growing set of phenotypes in a multi-site research study. Phenotyping that is accessible to investigators and IT personnel may enable its broader adoption.